Mapping Swiss German dialects

Loading libraries

library(tmap)
library(sf)
library(pophelper)
library(dplyr)
library(RColorBrewer)

Reading data

Read combined results with CLUMPP and merge them

#Read clumpp results
K6 <- readQ("../../Results/CLUMPP_output/pop_K6-combined-merged.txt")

#Merging results of 6 populations
merged_data <- mergeQ(K6)[[1]]

#Import coordinates
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coord<-read.table('../coordinates_wgs84.txt',header = T)
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coord<-read.table('../coordinates_wgs84.txt',header = T)
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#Spatialize results
k6_results<-cbind(coord,merged_data)

#Convert data to sf format
point_data<-st_as_sf(k6_results, coords = c("X_1", "Y_1"), crs = 4326)

#Read municipalities
municipalities <- st_read(
  "../../shapefiles/municipalities_voronoi.shp")

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## Reading layer `municipalities_voronoi' from data source `/home/noe/Desktop/Paper/Romano_PdM/shapefiles/municipalities_voronoi.shp' using driver `ESRI Shapefile'
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## Reading layer `municipalities_voronoi' from data source `/home/noe/Desktop/Paper/Romano_PdM/shapefiles/municipalities_voronoi.shp' using driver `ESRI Shapefile'
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## Simple feature collection with 356 features and 0 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 7.025109 ymin: 45.91675 xmax: 10.09691 ymax: 47.80846
## CRS:            4326
#Create ID in municipalities
municipalities$id <- 1:nrow(municipalities)

Results


Population 1

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mypaletTESS<-c("#1F78B4","#33A02C","#E31A1C","#FF7F00","#6A3D9A",'deeppink1')
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mypaletTESS<-c("#1F78B4","#33A02C","#E31A1C","#FF7F00","#6A3D9A",'deeppink1')
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palette <-colorRampPalette(c("white", mypaletTESS[6]))

population_1_aggregated = municipalities %>%
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  st_join(point_data[,c('Cluster1','geometry')]) %>%
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  st_join(point_data[,c('Cluster1','geometry')]) %>%
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  group_by(id) %>%
  summarize(Cluster1 = mean(Cluster1, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_1_aggregated) +
tm_polygons("Cluster1", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
) 
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Population 2

palette <-colorRampPalette(c("white", mypaletTESS[4]))

population_2_aggregated = municipalities %>%
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  st_join(point_data[,c('Cluster2','geometry')]) %>%
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  st_join(point_data[,c('Cluster2','geometry')]) %>%
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  group_by(id) %>%
  summarize(Cluster2 = mean(Cluster2, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_2_aggregated) +
tm_polygons("Cluster2", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
) 
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Population 3

palette <-colorRampPalette(c("white", mypaletTESS[5]))

population_3_aggregated = municipalities %>%
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  st_join(point_data[,c('Cluster3','geometry')]) %>%
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  st_join(point_data[,c('Cluster3','geometry')]) %>%
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  group_by(id) %>%
  summarize(Cluster3 = mean(Cluster3, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_3_aggregated) +
tm_polygons("Cluster3", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
) 
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Population 4

palette <-colorRampPalette(c("white", mypaletTESS[3]))

population_4_aggregated = municipalities %>%
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  st_join(point_data[,c('Cluster4','geometry')]) %>%
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  st_join(point_data[,c('Cluster4','geometry')]) %>%
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  group_by(id) %>%
  summarize(Cluster4 = mean(Cluster4, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_4_aggregated) +
tm_polygons("Cluster4", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
) +
   tm_layout(frame = F, main.title = "Admixture proportions",legend.outside = T)
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=======


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Population 5

palette <-colorRampPalette(c("white", mypaletTESS[2]))

population_5_aggregated = municipalities %>%
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  st_join(point_data[,c('Cluster5','geometry')]) %>%
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  st_join(point_data[,c('Cluster5','geometry')]) %>%
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  group_by(id) %>%
  summarize(Cluster5 = mean(Cluster5, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_5_aggregated) +
tm_polygons("Cluster5", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
) +
   tm_layout(frame = F, main.title = "Admixture proportions",legend.outside = T)
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=======


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Population 6

palette <-colorRampPalette(c("white", mypaletTESS[1]))

population_6_aggregated = municipalities %>%
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  st_join(point_data[,c('Cluster6','geometry')]) %>%
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  st_join(point_data[,c('Cluster6','geometry')]) %>%
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  group_by(id) %>%
  summarize(Cluster6 = mean(Cluster6, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_6_aggregated) +
tm_polygons("Cluster6", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
) +
   tm_layout(frame = F, main.title = "Admixture proportions",legend.outside = T)
<<<<<<< HEAD


=======


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